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A pitch tracking algorithm is described which operates in the time domain from a conditioned linear prediction residual and applies dynamic programming to optimally determine both pitch and voicing. A set of candidate pitch values are derived from a correlation function applied to an LPC prediction residual which has been low pass filtered in voiced speech and high pass filtered in unvoiced speech by using a single pole filter based on the first reflection coefficient of LPC. A post processing technique using dynamic programming is used to obtain a smooth pitch contour. By incorporating the correlation values of the candidate pitch values, voicing state information and spectral change information into the penalty function of the dynamic programming, a voicing decision is obtained along with an optimum pitch value. This integrated pitch tracking algorithm is compared to three standard pitch tracking algorithms over a data base of 58 male and female speakers ranging from 6 to 87 years of age and is shown to exhibit superior performance.
Secrest et al. (Thu,) studied this question.